• Journal of Genetic Medicine
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• v.20 no.1
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• pp.1-5
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• 2023

Until now, rare disease studies have mainly been carried out by detecting simple variants such as single nucleotide substitutions and short insertions and deletions in protein-coding regions of disease-associated gene panels using diagnostic next-generation sequencing in association with patient phenotypes. However, several recent studies reported that the detection rate hardly exceeds 50% even when whole-exome sequencing is applied. Therefore, the necessity of introducing whole-genome sequencing is emerging to discover more diverse genomic variants and examine their association with rare diseases. When no diagnosis is provided by whole-genome sequencing, additional omics techniques such as RNA-seq also can be considered to further interrogate causal variants. This paper will introduce a description of these multi-omics techniques and their applications in rare disease studies.

• Food Science of Animal Resources
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• v.43 no.6
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• pp.1067-1086
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• 2023

With rapid advances in meat science in recent decades, changes in meat quality during the pre-slaughter phase of muscle growth and the post-slaughter process from muscle to meat have been investigated. Commonly used techniques have evolved from early physicochemical indicators such as meat color, tenderness, water holding capacity, flavor, and pH to various omic tools such as genomics, transcriptomics, proteomics, and metabolomics to explore fundamental molecular mechanisms and screen biomarkers related to meat quality and taste characteristics. This review highlights the application of omics and integrated multi-omics in meat quality and taste characteristics studies. It also discusses challenges and future perspectives of multi-omics technology to improve meat quality and taste. Consequently, multi-omics techniques can elucidate the molecular mechanisms responsible for changes of meat quality at transcriptome, proteome, and metabolome levels. In addition, the application of multi-omics technology has great potential for exploring and identifying biomarkers for meat quality and quality control that can make it easier to optimize production processes in the meat industry.

• Korean Journal of Ecology and Environment
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• v.51 no.1
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• pp.16-28
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• 2018

Although physiological and ecological characteristics of cyanobacteria have been studied extensively for decades, unknown areas still remain greater than the already known. Recently, the development of omics techniques based on molecular biology has made it possible to view the ecosystem from a new and holistic perspective. The molecular mechanism of toxin production is being widely investigated, by comparative genomics and the transcriptomic studies. Biological interaction between bacteria and cyanobacteria is also explored: how their interactions and genetic biodiversity change depending on seasons and environmental factors, and how these interactions finally affect each component of ecosystem. Bioinformatics techniques have combined with ecoinformatics and omics data, enabling us to understand the underlying complex mechanisms of ecosystems. Particularly omics started to provide a whole picture of biological responses, occurring from all layers of hierarchical processes from DNA to metabolites. The expectation is growing further that algal blooms could be controlled more effectively in the near future. And an important insight for the successful bloom control would come from a novel blueprint drawn by omics studies.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.8_spc
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• pp.1321-1330
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• 2019

Recent development of novel techniques in systems biology have been used to improve and manipulate the rumen microbial ecosystem and gain a deeper understanding of its physiological and microbiological interactions and relationships. This provided a deeper insight and understanding of the relationship and interactions between the rumen microbiome and the host animal. New high-throughput techniques have revealed that the dominance of Proteobacteria in the neonatal gut might be derived from the maternal placenta through fetal swallowing of amniotic fluid in utero, which gradually decreases in the reticulum, omasum, and abomasum with increasing age after birth. Multi "omics" technologies have also enhanced rumen fermentation and production efficiency of dairy goats using dietary interventions through greater knowledge of the links between nutrition, metabolism, and the rumen microbiome and their effect in the environment. For example, supplementation of dietary lipid, such as linseed, affects rumen fermentation by favoring the accumulation of ${\alpha}$-linolenic acid biohydrogenation with a high correlation to the relative abundance of Fibrobacteriaceae. This provides greater resolution of the interlinkages among nutritional strategies, rumen microbes, and metabolism of the host animal that can set the foundation for new advancements in ruminant nutrition using multi 'omics' technologies.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.75-84
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• 2014

In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.

• IMMUNE NETWORK
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• v.14 no.2
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• pp.73-80
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• 2014

Because autoimmune diseases (AIDs) result from a complex combination of genetic and epigenetic factors, as well as an altered immune response to endogenous or exogenous antigens, systems biology approaches have been widely applied. The use of multi-omics approaches, including blood transcriptomics, genomics, epigenetics, proteomics, and metabolomics, not only allow for the discovery of a number of biomarkers but also will provide new directions for further translational AIDs applications. Systems biology approaches rely on high-throughput techniques with data analysis platforms that leverage the assessment of genes, proteins, metabolites, and network analysis of complex biologic or pathways implicated in specific AID conditions. To facilitate the discovery of validated and qualified biomarkers, better-coordinated multi-omics approaches and standardized translational research, in combination with the skills of biologists, clinicians, engineers, and bioinformaticians, are required.

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.220-220
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• 2018

• Mass Spectrometry Letters
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• v.15 no.2
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• pp.69 -78
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• 2024

The advancement of high-throughput omics technologies and systems biology is essential for understanding complex biological mechanisms and diseases. The integration of proteomics and metabolomics provides comprehensive insights into cellular functions and disease pathology, driven by developments in mass spectrometry (MS) technologies, including electrospray ionization (ESI). These advancements are crucial for interpreting biological systems effectively. However, integrating these technologies poses challenges. Compared to genomic, proteomics and metabolomics have limitations in throughput, and data integration. This review examines developments in MS equipped electrospray ionization (ESI), and their importance in the effective interpretation of biological mechanisms. The review also discusses developments in sample preparation, such as Simultaneous Metabolite, Protein, Lipid Extraction (SIMPLEX), analytical techniques, and data analysis, highlighting the application of these technologies in the study of cancer or Huntington's disease, underscoring the potential for personalized medicine and diagnostic accuracy. Efforts by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and integrative data analysis methods such as O2PLS and OnPLS extract statistical similarities between metabolomic and proteomic data. System modeling techniques that mathematically explain and predict system responses are also covered. This practical application also shows significant improvements in cancer research, diagnostic accuracy and therapeutic targeting for diseases like pancreatic ductal adenocarcinoma, non-small cell lung cancer, and Huntington's disease. These approaches enable researchers to develop standardized protocols, and interoperable software and databases, expanding multi-omics research application in clinical practice.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.9
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• pp.4229-4233
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• 2012

With the advances in modern diagnostic expertise for cancer, certain approaches allowing scanning of the complete genome and the proteome are becoming very useful for researchers. These high throughput techniques have already proven power, over traditional detection methods, in differentiating disease sub-types and identifying specific genetic events during progression of cancer. This paper introduces major branches of omics-technology and their applications in the field of cancer. It also addresses current road blocks that need to be overcome and future possibilities of these methods in oncogenic detection.

• Molecules and Cells
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• v.44 no.3
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• pp.127-135
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• 2021

Since the introduction of RNA sequencing (RNA-seq) as a high-throughput mRNA expression analysis tool, this procedure has been increasingly implemented to identify cell-level transcriptome changes in a myriad of model systems. However, early methods processed cell samples in bulk, and therefore the unique transcriptomic patterns of individual cells would be lost due to data averaging. Nonetheless, the recent and continuous development of new single-cell RNA sequencing (scRNA-seq) toolkits has enabled researchers to compare transcriptomes at a single-cell resolution, thus facilitating the analysis of individual cellular features and a deeper understanding of cellular functions. Nonetheless, the rapid evolution of high throughput single-cell "omics" tools has created the need for effective hypothesis verification strategies. Particularly, this issue could be addressed by coupling cell engineering techniques with single-cell sequencing. This approach has been successfully employed to gain further insights into disease pathogenesis and the dynamics of differentiation trajectories. Therefore, this review will discuss the current status of cell engineering toolkits and their contributions to single-cell and genome-wide data collection and analyses.